In this work, we take the homodyne detection discrete-modulated CV-QKD [1]} as an example to construct neural networks capable of predicting the secure key rate for the purpose of saving time and resource consumption. We apply our neural networks to a test set obtained at different excess noises and distances. Excellent accuracy and time savings are observed after adjusting the hyperparameters. Our work provides an end-to-end learning model that directly predicts the key rate after our neural networks are trained. Through some open source deep learning frameworks for on-device inference, such as TensorFlow Lite [2]}, our model can be easily deployed on devices at the edge of the network, such as mobile devices, embedded Linux, or microcontrollers.
